The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification. ANN inference has applications in tasks that require attention focusing. ANNs also have a niche to carve in clinical decision support, but their success depends crucially on better integration with clinical protocols, together with an awareness of the need to combine different paradigms to produce the simplest and most transparent overall reasoning structure, and the will to evaluate this in a real clinical environment. We have performed an assessment of the evidence for improvements in the use of ANN in lung cancer research. Our analysis showed that often the use of ANN in the medical literature had not been performed in an accurate manner. A strict cooperation between physician and biostatisticians could be helpful in determine and resolve these errors.